# this cell is tagged as parameters for `papermill` parameterization
#input configs
altair_config = None
nipah_config = None

#input files
entropy_file = None
func_scores_E2_file = None
binding_E2_file = None
func_scores_E3_file = None
binding_E3_file = None

#output files
filtered_E2_binding_data = None
filtered_E3_binding_data = None
filtered_E2_binding_low_effect = None
filtered_E3_binding_low_effect = None

#output images
entry_binding_combined_corr_plot = None
entry_binding_combined_corr_plot_agg = None
E2_E3_correlation = None
E2_E3_correlation_site = None
combined_E2_E3_site_corr = None
binding_by_site_plot = None
entry_binding_corr_heatmap = None
binding_corr_heatmap = None
binding_region_boxplot_plot = None
binding_region_bubble_plot = None
max_binding_in_stalk = None
max_binding_in_contact = None

# Parameters
nipah_config = "nipah_config.yaml"
altair_config = "data/custom_analyses_data/theme.py"
entropy_file = "results/entropy/entropy.csv"
func_scores_E2_file = "results/func_effects/averages/CHO_EFNB2_low_func_effects.csv"
binding_E2_file = "results/receptor_affinity/averages/EFNB2_monomeric_mut_effect.csv"
func_scores_E3_file = "results/func_effects/averages/CHO_EFNB3_low_func_effects.csv"
binding_E3_file = "results/receptor_affinity/averages/EFNB3_dimeric_mut_effect.csv"
filtered_E2_binding_data = "results/filtered_data/E2_binding_filtered.csv"
filtered_E3_binding_data = "results/filtered_data/E3_binding_filtered.csv"
filtered_E2_binding_low_effect = (
    "results/filtered_data/E2_binding_low_effect_filter.csv"
)
filtered_E3_binding_low_effect = (
    "results/filtered_data/E3_binding_low_effect_filter.csv"
)
entry_binding_combined_corr_plot = (
    "results/images/entry_binding_combined_corr_plot.html"
)
entry_binding_combined_corr_plot_agg = (
    "results/images/entry_binding_combined_corr_plot_agg.html"
)
E2_E3_correlation = "results/images/E2_E3_correlation.html"
E2_E3_correlation_site = "results/images/E2_E3_correlation_site.html"
combined_E2_E3_site_corr = "results/images/combined_E2_E3_site_corr.html"
binding_by_site_plot = "results/images/binding_by_site_plot.html"
entry_binding_corr_heatmap = "results/images/entry_binding_corr_heatmap.html"
binding_corr_heatmap = "results/images/binding_corr_heatmap.html"
binding_region_boxplot_plot = "results/images/binding_region_boxplot_plot.html"
binding_region_bubble_plot = "results/images/binding_region_bubble_plot.html"
max_binding_in_contact = "results/images/max_binding_in_contact.html"
max_binding_in_stalk = "results/images/max_binding_in_stalk.html"

import math
import os
import re
import altair as alt
import numpy as np
import pandas as pd
import scipy.stats
import yaml

# allow more rows for Altair
_ = alt.data_transformers.disable_max_rows()

if os.getcwd() == '/fh/fast/bloom_j/computational_notebooks/blarsen/2023/Nipah_Malaysia_RBP_DMS/':
    pass
    print("Already in correct directory")
else:
    os.chdir("/fh/fast/bloom_j/computational_notebooks/blarsen/2023/Nipah_Malaysia_RBP_DMS/")
    print("Setup in correct directory")

Setup in correct directory

if nipah_config is None:
##hard paths in case don't want to run with snakemake
    print('loading hard paths')
    altair_config = "data/custom_analyses_data/theme.py"
    nipah_config = "nipah_config.yaml"
    entropy_file = 'results/entropy/entropy.csv'
    
    #input files
    func_scores_E2_file = "results/func_effects/averages/CHO_EFNB2_low_func_effects.csv"
    binding_E2_file = "results/receptor_affinity/averages/EFNB2_monomeric_mut_effect.csv"
    func_scores_E3_file = "results/func_effects/averages/CHO_EFNB3_low_func_effects.csv"
    binding_E3_file = "results/receptor_affinity/averages/EFNB3_dimeric_mut_effect.csv"

    filtered_E2_binding_data="results/filtered_data/E2_binding_filtered.csv"
    filtered_E3_binding_data="results/filtered_data/E3_binding_filtered.csv"
    filtered_E2_binding_low_effect="results/filtered_data/E2_binding_low_effect_filter.csv"
    filtered_E3_binding_low_effect="results/filtered_data/E3_binding_low_effect_filter.csv"

if altair_config:
    with open(altair_config, 'r') as file:
        exec(file.read())

with open(nipah_config) as f:
    config = yaml.safe_load(f)

#import binding and entry data
e2 = pd.read_csv(binding_E2_file)
e2_func = pd.read_csv(func_scores_E2_file)
e3 = pd.read_csv(binding_E3_file)
e3_func = pd.read_csv(func_scores_E3_file)

def merge_func_binding_dfs(func,binding,name):
    df_int = pd.merge(
        binding,
        func,
        on=['site','mutant','wildtype'],
        suffixes=['_binding','_cell_entry'],
        validate='one_to_one',
        how='outer'
    ).round(3)
    df = df_int.rename(columns={'Ephrin binding_mean':'binding_mean','Ephrin binding_std':'binding_std','Ephrin binding_median':'binding_median'})

    # Only save relevant columns
    df = df[['site','wildtype','mutant','binding_median','binding_std','times_seen_binding','effect','effect_std','times_seen_cell_entry','frac_models']]
    
    def filter_binding_data(df):
        df_filter = df[
            (df['mutant'] != '*') &
            (df['mutant'] != '-') &
            (df['site'] != 603) &
            # Filter cell entry parameters
            (df['effect'] >= config['min_func_effect_for_binding']) &
            (df['times_seen_cell_entry'] >= config['func_times_seen_cutoff']) &
            (df['effect_std'] <= config['func_std_cutoff']) &
            # Filter binding parameters
            (df['times_seen_binding'] >= config['min_times_seen_binding']) &
            (df['binding_std'] <= config['max_binding_std']) &
            (df['frac_models'] >= config['frac_models'])
        ]
        return df_filter

    df_filter = filter_binding_data(df)
    
    #For pulling out low effect mutants for heatmaps later. Find mutants below func effect cutoff, but still have ok times_seen and func_std.
    def store_filtered_info(df):
        df_low_filter = df[
            (df['mutant'] != '*') &
            (df['mutant'] != '-') &
            (df['site'] != 603) &
            (df['effect'] < config['min_func_effect_for_binding']) &
            (df['times_seen_cell_entry'] >= config['func_times_seen_cutoff']) &
            (df['effect_std'] <= config['func_std_cutoff']) 
        ]
        return df_low_filter
    
    df_low_effect_filter = store_filtered_info(df)
    
    if name == 'EFNB2':
        print(name)
        df_filter.to_csv(filtered_E2_binding_data,index=False)
        df_low_effect_filter.to_csv(filtered_E2_binding_low_effect,index=False)
    else:
        df_filter.to_csv(filtered_E3_binding_data,index=False)
        df_low_effect_filter.to_csv(filtered_E3_binding_low_effect,index=False)
    
    return df_filter,df_low_effect_filter

#Call filtering function
df_E2_filter,df_E2_filter_missing = merge_func_binding_dfs(e2_func,e2,'EFNB2')
df_E3_filter,df_E3_filter_missing = merge_func_binding_dfs(e3_func,e3,'EFNB3')

#Now that they are filtered, merge EFNB2 and EFNB3
df_binding_effect_merge = pd.merge(
    df_E2_filter,
    df_E3_filter,
    on=['site','wildtype','mutant'],
    suffixes=['_E2','_E3'],
    how='outer'
)

#display stats
display(df_binding_effect_merge.describe().round(3))

# Make a concat df of E2/E3 data for plotting later
df_E2_filter['selection'] = 'EFNB2'
df_E3_filter['selection'] = 'EFNB3'
df_binding_effect_concat = pd.concat([df_E2_filter,df_E3_filter])

EFNB2

def plot_corr_binding_entry_updated(df,flag):
    variant_selector = alt.selection_point(
        on="mouseover",
        empty=False,
        fields=["site","mutant"],
        value=0
    )  
    variant_selector_agg = alt.selection_point(
        on="mouseover",
        empty=False,
        fields=["site"],
        value=0
    )  
    slider = alt.binding_range(min=2, max=10, step=1, name="times seen")
    selector = alt.param(name="SelectorName", value=2, bind=slider)

    empty_chart = []
    for cell in list(df['selection'].unique()):
        tmp_df = df[df['selection'] == cell]
        if flag == True:
            agg_df = tmp_df.groupby('site')[['binding_median','effect']].sum().reset_index()
            chart = alt.Chart(agg_df).mark_point(stroke='black',filled=True).encode(
                x=alt.X('effect', title=f'Summed {cell} Cell Entry', axis=alt.Axis(grid=True)),
                y=alt.Y('binding_median', title=f'Summed {cell} Binding', axis=alt.Axis(grid=True)),
                opacity=alt.condition(variant_selector_agg, alt.value(1), alt.value(0.2)),
                size=alt.condition(variant_selector_agg,alt.value(100),alt.value(50)),
                strokeWidth=alt.condition(variant_selector_agg,alt.value(1),alt.value(0)),
                color=alt.condition(variant_selector_agg,alt.value('orange'),alt.value('black')),
                tooltip=['site', 'binding_median','effect'],
            ).add_params(variant_selector_agg)
            
            empty_chart.append(chart)
        
        
        else:
            chart = alt.Chart(tmp_df).mark_point(stroke='black',filled=True).encode(
                x=alt.X('effect', title=f'{cell} Cell Entry', axis=alt.Axis(grid=True)),
                y=alt.Y('binding_median', title=f'{cell} Binding', axis=alt.Axis(grid=True)),
                opacity=alt.condition(variant_selector, alt.value(1), alt.value(0.1)),
                size=alt.condition(variant_selector,alt.value(50),alt.value(20)),
                strokeWidth=alt.condition(variant_selector,alt.value(1),alt.value(0)),
                color=alt.condition(variant_selector,alt.value('orange'),alt.value('black')),
                tooltip=['site', 'wildtype', 'mutant','binding_median','times_seen_binding','effect'],
            ).add_params(variant_selector)
            empty_chart.append(chart)
    
    combined_chart = alt.hconcat(*empty_chart,title=alt.Title('Correlation between binding and entry'))
    return combined_chart

entry_binding_corr_plot = plot_corr_binding_entry_updated(df_binding_effect_concat,False)
entry_binding_corr_plot.display()
if entry_binding_combined_corr_plot is not None:
    entry_binding_corr_plot.save(entry_binding_combined_corr_plot)

entry_binding_corr_plot_agg = plot_corr_binding_entry_updated(df_binding_effect_concat,True)
entry_binding_corr_plot_agg.display()
if entry_binding_combined_corr_plot is not None:
    entry_binding_corr_plot_agg.save(entry_binding_combined_corr_plot_agg)

def plot_entry_binding_corr_heatmap(df):
    empty_chart = []
    
    for cell in list(df['selection'].unique()):
        tmp_df = df[df['selection'] == cell]
        chart = alt.Chart(tmp_df,title=f'{cell}').mark_rect().encode(
            x=alt.X('effect',title='Cell Entry',axis=alt.Axis(values=[-2,-1,0,1])).bin(maxbins=60),
            y=alt.Y('binding_median',title='Binding',axis=alt.Axis(values=[-4,-2,0,2])).bin(maxbins=60),
            color=alt.Color('count()',title='Count').scale(scheme='greenblue'),
        )
        empty_chart.append(chart)
    
    combined_chart = alt.hconcat(*empty_chart,title=alt.Title('Correlation between binding and entry')).resolve_scale(y='shared',x='shared',color='shared')
    return combined_chart

entry_binding_corr_heat = plot_entry_binding_corr_heatmap(df_binding_effect_concat)
entry_binding_corr_heat.display()
if entry_binding_combined_corr_plot is not None:
    entry_binding_corr_heat.save(entry_binding_corr_heatmap)

def overall_stats(df,effect,name):
    #Find quantiles
    quantile_2 = df['binding_median'].quantile(.02)
    quantile_98 = df['binding_median'].quantile(.98)
    print(f'The 2% quantile for {name} is: {quantile_2}')
    print(f'The 98% quantile for {name} is: {quantile_98}')

    #Now group sites and find intolerant sites 
    filtered_df = df.groupby('site').filter(lambda group: (group[effect] <-0.25).all())
    unique = filtered_df['site'].unique()
    # Convert unique to a Pandas Series
    unique_series = pd.Series(unique)
    # Find the common elements
    unique_contact_bool = unique_series.isin(config['contact_sites'])
    # Filter and get the common elements
    common_elements = unique_series[unique_contact_bool]

    # Print the common elements
    print(f'Here are the contact sites that are conserved: {common_elements}')
    
    print(f'There are {len(unique)} sites with all negative binding score mutants for {name}')
    print(f'These are the sites for {name} with all negative binding score mutants: {list(unique)}')

    #Now find sites with low and high binding (median)
    median_df = df.groupby('site')['binding_median'].median().reset_index().sort_values(by='binding_median')
    print(f'For {name}, these are the sites with lowest median binding scores: {median_df.head(5)}')
    median_df = df.groupby('site')['binding_median'].median().reset_index().sort_values(by='binding_median',ascending=False)
    print(f'For {name}, these are the sites with highest median binding scores: {median_df.head(5)}')
    
    #Now calculate mutant number
    total_mutants = df.shape[0]
    upper_cutoff = df[df[effect] > 1].sort_values(by='binding_median',ascending=False)
    median_upper = upper_cutoff['effect'].median()
    print(f'The median entry score for top binders was: {median_upper}')
    
    mutants_above_cutoff_tolerated = upper_cutoff[upper_cutoff['effect'] > 0]
    mutants_above_cutoff_tolerated = mutants_above_cutoff_tolerated[['site','effect','binding_median','wildtype','mutant']]
    print(f'The mutants with positive entry scores and good binding are: {mutants_above_cutoff_tolerated.head(5)}')
    
    lower_cutoff = df[df[effect] < -1]
    
    print(f'For {name}, there are a total of : {total_mutants} binding mutants')
    print(f'For {name}, there are {upper_cutoff.shape[0]} mutants above cutoff, and {mutants_above_cutoff_tolerated.shape[0]} that have good entry scores')
    print(f'For {name}, there are {lower_cutoff.shape[0]} mutants below cutoff')
 
    total_sites = df['site'].unique().shape[0]
    
    print(f'The total number of sites are: {total_sites}')


overall_stats(df_E2_filter,'binding_median','E2')
overall_stats(df_E3_filter,'binding_median','E3')

The 2% quantile for E2 is: -3.9003
The 98% quantile for E2 is: 1.2080999999999995
Here are the contact sites that are conserved: 4     240
5     242
10    389
21    490
22    491
27    501
28    504
29    505
31    531
32    532
33    533
34    557
35    579
36    581
39    588
dtype: int64
There are 41 sites with all negative binding score mutants for E2
These are the sites for E2 with all negative binding score mutants: [116, 220, 236, 238, 240, 242, 243, 248, 346, 351, 389, 390, 398, 400, 438, 439, 441, 460, 467, 486, 487, 490, 491, 494, 495, 497, 500, 501, 504, 505, 526, 531, 532, 533, 557, 579, 581, 584, 585, 588, 590]
For E2, these are the sites with lowest median binding scores:      site  binding_median
445   533         -4.0450
408   494         -4.0250
406   491         -3.9815
405   490         -3.9410
403   487         -3.8680
For E2, these are the sites with highest median binding scores:      site  binding_median
33    104          1.4065
48    120          1.3630
128   204          1.1770
49    122          1.1750
250   331          1.1000
The median entry score for top binders was: -0.925
The mutants with positive entry scores and good binding are:       site  effect  binding_median wildtype mutant
1324   139   0.005           1.989        N      Y
5448   354   0.228           1.498        S      T
9533   566   0.081           1.415        F      H
7170   444   0.163           1.256        I      F
1217   134   0.099           1.249        S      I
For E2, there are a total of : 6946 binding mutants
For E2, there are 229 mutants above cutoff, and 22 that have good entry scores
For E2, there are 1039 mutants below cutoff
The total number of sites are: 513
The 2% quantile for E3 is: -0.652
The 98% quantile for E3 is: 0.606
Here are the contact sites that are conserved: 3    389
7    501
8    505
9    531
dtype: int64
There are 10 sites with all negative binding score mutants for E3
These are the sites for E3 with all negative binding score mutants: [108, 140, 352, 389, 486, 494, 497, 501, 505, 531]
For E3, these are the sites with lowest median binding scores:      site  binding_median
414   501         -0.9160
441   531         -0.7235
307   389         -0.6870
489   584         -0.6810
416   505         -0.6660
For E3, these are the sites with highest median binding scores:      site  binding_median
494   589          0.6805
56    129          0.5080
59    132          0.4675
154   231          0.4310
446   537          0.4275
The median entry score for top binders was: -0.7605
The mutants with positive entry scores and good binding are:        site  effect  binding_median wildtype mutant
7995    492   0.496           1.200        Q      L
2632    211   0.037           1.149        G      F
10015   598   0.344           1.141        P      G
1655    161   0.234           1.011        S      E
For E3, there are a total of : 6648 binding mutants
For E3, there are 26 mutants above cutoff, and 4 that have good entry scores
For E3, there are 12 mutants below cutoff
The total number of sites are: 508

#find sites that are different
def find_biggest_differences(df):
    efnb2_good_efnb3_bad = df[
        (df['binding_median_E2'] > 0.1) &
        (df['binding_median_E3'] < -0.1)
    ].sort_values(by='binding_median_E2',ascending=False)
    display(efnb2_good_efnb3_bad)
    efnb2_bad_efnb3_good = df[
        (df['binding_median_E2'] < -0.1) &
        (df['binding_median_E3'] > 0.1)
    ].sort_values(by='binding_median_E3',ascending=False)
    display(efnb2_bad_efnb3_good)

find_biggest_differences(df_binding_effect_merge)

def plot_entry_binding_corr(df):
    chart = alt.Chart(df,title='Correlation Between Mutant Binding Scores').mark_rect().encode(
        x=alt.X('binding_median_E2',title='EFNB2 binding',axis=alt.Axis(values=[-5,0,2])).bin(maxbins=40),
        y=alt.Y('binding_median_E3',title='EFNB3 binding',axis=alt.Axis(values=[-2,0,2])).bin(maxbins=40),
        color=alt.Color('count()',title='Count').scale(scheme='greenblue'),
    )
    return chart

entry_binding_corr_heatmap_1 = plot_entry_binding_corr(df_binding_effect_merge)
entry_binding_corr_heatmap_1.display()
if entry_binding_combined_corr_plot is not None:
    entry_binding_corr_heatmap_1.save(binding_corr_heatmap)

def plot_affinity_solid(df):
    df = df.dropna()
    # calculate r value
    slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(df['binding_median_E2'], df['binding_median_E3'])
    r_value = float(r_value)
    # make chart
    chart = alt.Chart(df,title=alt.Title('Correlation between Mutant Binding Scores',subtitle=f'r={r_value:.2f}')).mark_point(color='black',size=30, opacity=0.2,filled=True).encode(
        x=alt.X('binding_median_E2', title=('EFNB2 Binding')),
        y=alt.Y('binding_median_E3', title=('EFNB3 Binding')),
        tooltip=['site', 'wildtype','mutant','binding_median_E2','binding_median_E3','effect_E2','effect_E3'],
    )
    min = int(df['binding_median_E2'].min())
    max = int(df['binding_median_E3'].max())
    text = alt.Chart({'values':[{'x': min, 'y': max, 'text': f'r = {r_value:.2f}'}]}).mark_text(
        align='left', baseline='top', dx=-10, dy=-20).encode(
            x=alt.X('x:Q'),
            y=alt.Y('y:Q'),
            text='text:N'
        )
    chart_and_text = chart
    return chart_and_text

E2_E3_corr = plot_affinity_solid(df_binding_effect_merge)
E2_E3_corr.display()
if entry_binding_combined_corr_plot is not None:
    E2_E3_corr.save(E2_E3_correlation)

def plot_affinity_solid_mean(df):
    df = df.dropna()
    means = df.groupby('site').agg({
            'effect_E2': 'median',
            'effect_E3': 'median',
            'binding_median_E2': 'median',
            'binding_median_E3': 'median',
            'wildtype': 'first'
        }).reset_index()
    slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(means['binding_median_E2'], means['binding_median_E3'])
    r_value = float(r_value)
    chart = alt.Chart(means,title=alt.Title('Correlation between Aggregate Mutant Binding Scores',subtitle=f'r={r_value:.2f}')).mark_point(size=50, opacity=0.3).encode(
            x=alt.X('binding_median_E2', title=('Median EFNB2 Binding'), axis=alt.Axis(tickCount=3)),
            y=alt.Y('binding_median_E3', title=('Median EFNB3 Binding'), axis=alt.Axis(tickCount=3)),
            tooltip=['site', 'wildtype','binding_median_E2','binding_median_E3','effect_E2','effect_E3'],
        )
    text = alt.Chart({'values':[{'x': -3.5, 'y': 0.5, 'text': f'r = {r_value:.2f}'}]}).mark_text(
        align='left', baseline='top', dx=0, dy=-10).encode(
            x=alt.X('x:Q'),
            y=alt.Y('y:Q'),
            text='text:N'
        )
    chart_and_text = chart 
    return chart_and_text

E2_E3_site_corr = plot_affinity_solid_mean(df_binding_effect_merge)
E2_E3_site_corr.display()
if entry_binding_combined_corr_plot is not None:
    E2_E3_site_corr.save(E2_E3_correlation_site)

if entry_binding_combined_corr_plot is not None:
    (E2_E3_site_corr | E2_E3_corr).save(combined_E2_E3_site_corr)

def plot_affinity_by_site_median(df):
    variant_selector = alt.selection_point(
        on="mouseover",
        nearest=True,
        empty=False,
        fields=["site"],
        value=0
    )  
    empty_charts = []
    for selection in ['binding_median_E2','binding_median_E3']:
        if selection == 'binding_median_E2':
            name = 'EFNB2 Binding'
        else:
            name = 'EFNB3 Binding'
        mean = df.groupby('site')[selection].sum().reset_index()
        chart = alt.Chart(mean).mark_point(stroke='black',filled=True).encode(
            x=alt.X('site', title=('Site'), axis=alt.Axis(grid=True, tickCount=4),scale=alt.Scale(domain=[70,602])),
            y=alt.Y(selection, title=(name), axis=alt.Axis(grid=True, tickCount=4)),
            tooltip=['site'],
            color=alt.condition(variant_selector, alt.value('orange'), alt.value('black')),
            opacity=alt.condition(variant_selector, alt.value(1), alt.value(0.2)),
            strokeWidth=alt.condition(variant_selector,alt.value(2),alt.value(0))
        ).add_params(variant_selector)
        empty_charts.append(chart)
    combined_chart = alt.vconcat(*empty_charts, spacing=1,title='Summed Binding by Site')
    return combined_chart



binding_by_site = plot_affinity_by_site_median(df_binding_effect_merge)
binding_by_site.display()
if entry_binding_combined_corr_plot is not None:
    binding_by_site.save(binding_by_site_plot)

def plot_affinity_by_contact_site(df,sites_to_show,title_text):
    variant_selector = alt.selection_point(
        on="mouseover",
        nearest=True,
        empty=False,
        fields=["site"],
        value=0
    )  
    empty_charts = []

    contact_df = df[df['site'].isin(sites_to_show)]
    sites = list(contact_df['site'].unique())
    
    for selection in df['selection'].unique():
        tmp_df = contact_df[contact_df['selection'] == selection]
        mean = tmp_df.groupby('site')['binding_median'].max().reset_index()
        
        chart = alt.Chart(mean).mark_point(size=100).encode(
            x=alt.X('site:O', sort=sites,title=('Site'), axis=alt.Axis(grid=True, labelAngle=-90),scale=alt.Scale(domain=sites)),
            y=alt.Y('binding_median', title=(f'{selection}'), axis=alt.Axis(grid=True)),
            tooltip=['site'],
            color=alt.condition(variant_selector, alt.value('orange'), alt.value('black')),
            strokeWidth=alt.condition(variant_selector,alt.value(2),alt.value(0))
        ).add_params(variant_selector)
        empty_charts.append(chart)
    combined_chart = alt.vconcat(*empty_charts, spacing=1,title=title_text)
    return combined_chart



contact_binding_by_site = plot_affinity_by_contact_site(df_binding_effect_concat,config['contact_sites'],'Max Binding in Contact')
contact_binding_by_site.display()
if entry_binding_combined_corr_plot is not None:
    contact_binding_by_site.save(max_binding_in_contact)

contact_binding_by_site_stalk = plot_affinity_by_contact_site(df_binding_effect_concat,list(range(96, 147)),"Max Binding in Stalk")
contact_binding_by_site_stalk.display()
if entry_binding_combined_corr_plot is not None:
    contact_binding_by_site_stalk.save(max_binding_in_stalk)

def make_boxplot_binding_region(df,title):# Create a box plot using Altair for aggregated means
    barrel_ranges = {
    'Hydrophobic': config['hydrophobic'],
    'Salt Bridges': config['salt_bridges'],
    'Hydrogen Bonds': config['h_bond_total'],
    'Contact': config['contact_sites'],
    'Overall': list(range(71,602)),
    }
    
    mean_df = df.groupby('site')[['binding_median']].median().reset_index()
    custom_order = ['Hydrophobic','Salt Bridges','Hydrogen Bonds','Contact','Overall']
    agg_means = []
    
    # For each barrel, filter the site_means dataframe to the sites belonging to that barrel and then store the means
    for barrel, sites in barrel_ranges.items():
        subset = mean_df[mean_df['site'].isin(sites)]
        for _, row in subset.iterrows():
            agg_means.append({'barrel': barrel, 'effect': row['binding_median'],'site':row['site']})
        agg_means_df = pd.DataFrame(agg_means)
    chart = alt.Chart(agg_means_df).mark_point(size=50,opacity=0.4).encode(
                x=alt.X('barrel:O', sort=custom_order,title=None,axis=alt.Axis(labelAngle=-90)),
                y=alt.Y('effect',title=f'Median {title} Binding',axis=alt.Axis(grid=True,tickCount=4)),
                xOffset='random:Q',
                tooltip=['barrel', 'effect','site'],
            ).transform_calculate(
                random="sqrt(-1*log(random()))*cos(2*PI*random())"
        
            )
    
    return chart.display()

make_boxplot_binding_region(df_E2_filter,'EFNB2')
make_boxplot_binding_region(df_E3_filter,'EFNB3')

def make_boxplot_binding_region(df):
    barrel_ranges = {
        "Stalk": list(range(96, 147)),
        "Neck": list(range(148, 165)),
        "Linker": list(range(166, 177)),
        "Head": list(range(178, 602)),
        'Receptor Contact': config['contact_sites'],
        "Total": list(range(71, 602)),
    }
    custom_order = ["Stalk", "Neck", "Linker", "Head", "Receptor Contact", "Total"]
    empty_charts = []
    for selection in df['selection'].unique():
        tmp_df = df[df["selection"] == selection]
        agg_means = []

        # For each barrel, filter the site_means dataframe to the sites belonging to that barrel and then store the means
        for barrel, sites in barrel_ranges.items():
            subset = tmp_df[tmp_df["site"].isin(sites)]
            for _, row in subset.iterrows():
                agg_means.append(
                    {"region": barrel, "binding_median": row["binding_median"], "site": row["site"]}
                )
            agg_means_df = pd.DataFrame(agg_means)

        chart = (
            alt.Chart(agg_means_df, title=f"{selection}")
            .mark_boxplot(color="darkgray", extent="min-max", opacity=1)
            .encode(
                x=alt.X(
                    "region:O",
                    sort=custom_order,
                    title="RBP Region",
                    axis=alt.Axis(labelAngle=-90),
                ),
                y=alt.Y(
                    "binding_median",
                    title=f"Binding",
                    axis=alt.Axis(grid=True, tickCount=4),
                ),
                tooltip=["region", "binding_median", "site"],
            ).properties(width=config['width'],height=config['height'])
        )
        empty_charts.append(chart)
    combined_effect_chart = alt.hconcat(*empty_charts).resolve_scale(
        y="shared", x="shared", color="independent"
    )
    return combined_effect_chart


entry_region_boxplot = make_boxplot_binding_region(df_binding_effect_concat)
entry_region_boxplot.display()
if entry_binding_combined_corr_plot is not None:
    entry_region_boxplot.save(binding_region_boxplot_plot)

def make_bubble_binding_region(df):
    barrel_ranges = {
        "Stalk": list(range(96, 147)),
        "Neck": list(range(148, 165)),
        "Linker": list(range(166, 177)),
        "Head": list(range(178, 602)),
        'Receptor Contact': config['contact_sites'],
        "Total": list(range(71, 602)),
    }
    custom_order = ["Stalk", "Neck", "Linker", "Head", "Receptor Contact", "Total"]
    empty_charts = []
    for selection in df['selection'].unique():
        tmp_df = df[df["selection"] == selection]
        agg_means = []

        # For each barrel, filter the site_means dataframe to the sites belonging to that barrel and then store the means
        for barrel, sites in barrel_ranges.items():
            subset = tmp_df[tmp_df["site"].isin(sites)]
            for _, row in subset.iterrows():
                agg_means.append(
                    {"region": barrel, "binding_median": row["binding_median"], "site": row["site"],"mutant": row["mutant"]}
                )
            agg_means_df = pd.DataFrame(agg_means)
        
        variant_selector = alt.selection_point(
        on="mouseover", empty=False, fields=["site",'mutant'], value=1
        )
        
        chart = (
            alt.Chart(agg_means_df, title=f"{selection}")
            .mark_point(opacity=0.3, stroke='black')
            .encode(
                x=alt.X(
                    "region:O",
                    sort=custom_order,
                    title="RBP Region",
                    axis=alt.Axis(labelAngle=-90),
                ),
                y=alt.Y(
                    "binding_median",
                    title=f"Binding",
                    axis=alt.Axis(grid=True, tickCount=4),
                ),
                xOffset="random:Q",
                tooltip=["region", "binding_median", "site","mutant"],
                color=alt.condition(
                    variant_selector, alt.value("orange"), alt.value("black")
                ),
                opacity=alt.condition(variant_selector, alt.value(1), alt.value(0.1)),
                strokeWidth=alt.condition(variant_selector,alt.value(2),alt.value(0)),
                size=alt.condition(variant_selector,alt.value(50),alt.value(15)),
                                          
            ).transform_calculate(
                random="sqrt(-1*log(random()))*cos(2*PI*random())"
            ).properties(width=config['width'],height=config['height'])
        ).add_params(variant_selector)
        empty_charts.append(chart)
    combined_effect_chart = alt.hconcat(*empty_charts).resolve_scale(
        y="shared", x="shared", color="independent"
    ).add_params(variant_selector)
    return combined_effect_chart


entry_region_bubble = make_bubble_binding_region(df_binding_effect_concat)
entry_region_bubble.display()
if entry_binding_combined_corr_plot is not None:
    entry_region_bubble.save(binding_region_bubble_plot)

	site	binding_median_E2	binding_std_E2	times_seen_binding_E2	effect_E2	effect_std_E2	times_seen_cell_entry_E2	frac_models_E2	binding_median_E3	binding_std_E3	times_seen_binding_E3	effect_E3	effect_std_E3	times_seen_cell_entry_E3	frac_models_E3
count	7390.000	6946.000	6946.000	6946.000	6946.000	6946.000	6946.000	6946.000	6648.000	6648.000	6648.000	6648.000	6648.000	6648.000	6648.0
mean	342.855	-0.325	0.518	5.943	-0.141	0.374	7.383	0.997	-0.018	0.184	5.816	-0.099	0.397	6.404	1.0
std	148.710	1.113	0.346	2.943	0.601	0.191	4.142	0.028	0.278	0.184	2.813	0.565	0.186	3.308	0.0
min	71.000	-5.284	0.004	2.250	-2.000	0.018	2.000	0.750	-1.960	0.000	2.500	-1.997	0.044	2.000	1.0
25%	217.000	-0.410	0.277	4.000	-0.433	0.229	4.875	1.000	-0.152	0.061	4.000	-0.353	0.259	4.429	1.0
50%	341.000	-0.020	0.436	5.250	0.038	0.343	6.500	1.000	-0.014	0.136	5.500	0.066	0.367	5.714	1.0
75%	468.000	0.208	0.667	7.000	0.318	0.488	8.750	1.000	0.118	0.250	7.000	0.316	0.505	7.429	1.0
max	602.000	2.602	2.915	39.750	0.640	1.000	64.380	1.000	2.008	2.650	40.000	0.660	0.998	49.140	1.0

	site	wildtype	mutant	binding_median_E2	binding_std_E2	times_seen_binding_E2	effect_E2	effect_std_E2	times_seen_cell_entry_E2	frac_models_E2	binding_median_E3	binding_std_E3	times_seen_binding_E3	effect_E3	effect_std_E3	times_seen_cell_entry_E3	frac_models_E3
496	120	I	M	1.363	0.837	5.50	-0.638	0.447	6.750	1.0	-0.357	0.806	4.0	-0.197	0.222	5.714	1.0
1719	223	A	D	1.349	1.474	5.75	-1.337	0.410	10.000	1.0	-0.212	0.708	4.5	-1.718	0.397	7.429	1.0
940	170	E	T	1.312	0.886	4.25	-0.314	0.668	4.250	1.0	-0.136	0.235	4.0	-0.697	0.573	5.000	1.0
1090	179	S	P	1.268	0.727	5.25	-1.972	0.796	5.750	1.0	-0.102	0.188	5.5	-1.414	0.590	7.143	1.0
345	97	L	A	1.256	0.682	3.75	-1.752	0.677	4.875	1.0	-0.511	0.162	3.0	-1.756	0.231	5.571	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4860	446	D	F	0.104	0.201	5.25	0.361	0.280	7.125	1.0	-0.204	0.102	5.0	0.511	0.238	5.286	1.0
1718	223	A	C	0.103	0.426	4.50	0.226	0.252	7.250	1.0	-0.145	0.129	5.0	-0.063	0.337	5.429	1.0
6850	596	K	G	0.101	0.808	6.00	0.309	0.284	5.750	1.0	-0.277	0.027	4.5	0.390	0.270	6.143	1.0
4915	449	G	P	0.101	0.134	3.50	-0.342	0.453	4.500	1.0	-0.329	0.368	2.5	0.140	0.616	3.571	1.0
6403	562	I	E	0.101	0.279	2.75	-0.222	0.478	5.500	1.0	-0.339	0.506	3.0	0.307	0.441	4.000	1.0

	site	wildtype	mutant	binding_median_E2	binding_std_E2	times_seen_binding_E2	effect_E2	effect_std_E2	times_seen_cell_entry_E2	frac_models_E2	binding_median_E3	binding_std_E3	times_seen_binding_E3	effect_E3	effect_std_E3	times_seen_cell_entry_E3	frac_models_E3
5491	492	Q	L	-0.167	0.523	5.00	0.514	0.344	4.500	1.0	1.200	0.031	5.0	0.496	0.296	4.857	1.0
6873	598	P	G	-0.200	0.772	4.50	-1.051	0.970	4.500	1.0	1.141	1.564	4.0	0.344	0.542	5.000	1.0
6763	588	I	P	-2.239	0.805	4.75	-0.187	0.393	4.500	1.0	1.052	0.055	5.0	-0.637	0.369	5.000	1.0
143	81	A	W	-0.744	0.376	3.00	-1.744	0.344	5.375	1.0	0.921	0.131	3.0	-0.974	0.723	4.143	1.0
504	123	N	G	-0.162	0.646	3.25	-1.133	0.779	4.500	1.0	0.820	0.160	2.5	0.141	0.616	4.429	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
5649	507	V	M	-1.357	0.772	6.00	0.419	0.080	7.375	1.0	0.102	0.614	4.0	-0.441	0.506	5.143	1.0
2316	275	N	V	-1.259	0.628	3.75	-0.264	0.370	6.750	1.0	0.101	0.378	4.0	-0.121	0.246	4.143	1.0
3048	324	K	S	-0.203	0.732	4.00	-0.328	0.680	5.500	1.0	0.101	0.024	4.0	0.217	0.197	4.571	1.0
5783	518	N	E	-0.151	0.285	5.75	0.295	0.370	7.500	1.0	0.101	0.200	6.5	0.225	0.386	5.286	1.0
6453	567	L	F	-0.313	0.242	12.25	0.335	0.264	15.500	1.0	0.101	0.152	12.5	0.102	0.302	15.570	1.0

Analyze selection data using soluble Ephrin-B2 or -B3¶

Run config files to setup altair theme and config variables¶

Make the E2/E3 dataframes, filter separately, then merge¶

Filter the data and save¶

Make nice interactive plot for correlation between binding and entry for EFNB2 and EFNB3¶

Find sites with opposite effects on binding¶

Find correlations between EFNB2 and EFNB3 binding¶

Plot correlations between summary statistics for each site¶

Make plot showing binding by site (median)¶

Make bubble plots for binding in different areas of receptor pocket¶

make boxplot of binding scores by region¶